Employing a deep convolutional neural network for human activity recognition based on binary ambient sensor data
Gadelhag Mohmed, Ahmad Lotfi, Amir Pourabdollah
Abstract
Due to rising cost of social care, the number of older adults who prefer to live independently in their own home has increased. The independent lifestyle cannot be achieved if the elderly user suffers from mild cognitive impairment unless a suitable assistive environment is provided to monitor and recognise the daily activities. Different techniques are employed for gathering data representing the user's activities. Available systems with wearable sensors or camera devices are undesirable to many users due to privacy issues. This paper proposes the use of a Deep Convolutional Neural Network (DCNN) for human activity recognition using binary ambient sensors such as Passive Infrared (PIR) and door sensors. Each activity is represented as a binary string converted into a greyscale image. Uncorrelated features are selected and they are then used as inputs to an Adaptive Boosting (AdaBoost) and Fuzzy C-means (FCM) classifiers for recognising Activities of Daily Living (ADL). The performance of the proposed model is evaluated using a dataset representing the ADL for a single user. The achieved results using the extracted features from the greyscale image representing ADL with AdaBoost and FCM algorithms are 99.5% and 86.4%, respectively.